Rayleigh distribution expected value.
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we’ve built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]
[Rayleigh][rayleigh-distribution] distribution [expected value][expected-value].
math
\mathbb{E}\left[ X \right] = \sigma \sqrt{\dfrac{\pi}{2}}
σ > 0
is the scale parameter.bash
npm install @stdlib/stats-base-dists-rayleigh-mean
script
tag without installation and bundlers, use the [ES Module][es-module] available on the [esm
][esm-url] branch (see [README][esm-readme]).deno
][deno-url] branch (see [README][deno-readme] for usage intructions).umd
][umd-url] branch (see [README][umd-readme]).javascript
var mean = require( '@stdlib/stats-base-dists-rayleigh-mean' );
sigma
.javascript
var y = mean( 9.0 );
// returns ~11.28
y = mean( 1.5 );
// returns ~1.88
sigma < 0
, the function returns NaN
.javascript
var y = mean( -1.0 );
// returns NaN
y = mean( -0.1 );
// returns NaN
javascript
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var mean = require( '@stdlib/stats-base-dists-rayleigh-mean' );
var sigma;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
sigma = randu() * 20.0;
y = mean( sigma );
console.log( 'σ: %d, E(X,σ): %d', sigma.toFixed( 4 ), y.toFixed( 4 ) );
}
c
#include "stdlib/stats/base/dists/rayleigh/mean.h"
c
double out = stdlib_base_dists_rayleigh_mean( 9.0 );
// returns ~11.253
[in] double
scale parameter.c
double stdlib_base_dists_rayleigh_mean( const double sigma );
c
#include "stdlib/stats/base/dists/rayleigh/mean.h"
#include <stdlib.h>
#include <stdio.h>
static double random_uniform( const double min, const double max ) {
double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
return min + ( v * ( max - min ) );
}
int main( void ) {
double sigma;
double y;
int i;
for ( i = 0; i < 25; i++ ) {
sigma = random_uniform( 0.0, 20.0 );
y = stdlib_base_dists_rayleigh_mean( sigma );
printf( "σ: %lf, E(X;σ): %lf\n", sigma, y );
}
}